As artificial intelligence becomes increasingly accessible and new services launch daily, the demand for powerful GPUs is surging. New GPUs are undergoing constant production and enhancement to meet the growing needs of a wide array of AI applications.
Advanced services like Amazon Q, Google Gemini, and OpenAI’s ChatGPT rely heavily on these GPUs for their immense compute capacity and parallel processing abilities. This high demand can make the choice of the right GPU for specific workloads a challenging decision, especially when considering factors like the platform, pricing, and time constraints.
To clarify the landscape, we’ve created a guide to cloud GPUs. This guide will detail the various GPUs available on GCP, focusing on their unique capabilities, ideal use cases, and pricing structures.
Understanding Cloud GPUs
Cloud GPUs are specialized hardware accelerators designed to handle the intensive computational demands of various applications, particularly in AI and machine learning. These GPUs, available on cloud platforms like AWS and GCP, offer scalable power and efficiency for complex tasks, ranging from training large language models (LLMs) and foundational models (FMs) to powering generative AI applications.
They’re used for a variety of graphical and compute processing tasks like:
- Graphic-Intensive Tasks: The NVIDIA P100 and K80 GPUs are ideal for 3D rendering and video processing, leveraging their ability for efficient parallel processing.
- Scientific Research: Complex simulations and data analyses in scientific fields benefit from GPUs like the NVIDIA A100, offering robust computational abilities for handling large data sets.
- Energy-Efficient and Cost-Effective Operations: AWS Graviton chips exemplify the latest developments in energy-efficient yet powerful GPUs, suitable for a wide range of cloud workloads while managing operational costs.
Which NVIDIA GPU should I Use on Google Cloud Platform (GCP)?
When it comes to selecting an NVIDIA GPU on GCP, the decision hinges on a blend of factors including workload demands, performance requirements, time constraints, and budget considerations. GCP’s cloud GPUs, ranging from powerful models like the NVIDIA A100 to more cost-effective options like the T4, are tailored to accommodate a diverse array of computing tasks, each with its unique set of requirements.
The selection process also involves evaluating the compatibility of the GPU with specific cloud services like Compute Engine or Google Kubernetes Engine (GKE).
- For instance, certain GPUs might be better optimized for seamless integration with GKE for containerized applications, while others might offer more advantages when deployed within Compute Engine VMs for specific computational tasks.
Hence, it’s important to understand the pricing, discounts capabilities, and use case for every GPU available on GCP, as explored in the sections below.
GCP Cloud GPUs Discounts
There are three types of discounts that are applicable when using GPUs on GCP. They are:
Sustained Use Discounts (SUDs): Automatically applied to GPUs attached to standard VM instances for long-term use. Offers up to 30% off the regular price for GPUs that run throughout the month. Ideal for consistent workloads without significant fluctuations in resource requirements.
Committed Use Discounts (CUDs): Reduced pricing for users who commit to a specified period of GPU usage. Offers discounts of up to 57% for users committing to a specified period of GPU usage. Suitable for long-term projects with predictable resource needs.
Spot VMs Pricing: Lower costs for flexible workloads by utilizing Google Cloud’s excess capacity. Provides discounts up to 70% compared to on-demand prices. Best for non-critical applications that can tolerate interruptions, such as batch processing or development/testing environments.
In the next section, we will compare the pricing and capabilities of different Nvidia GPUs available on GCP. It is crucial for GCP users to familiarize themselves to pick the right instance for their workloads.
GCP NVIDIA GPUs Pricing and Discounts Comparison Chart
The following table provides a detailed comparison of NVIDIA GPUs available in GCP, factoring in performance, capacity, instance type, and pricing.
| NVIDIA GPU | Performance | Capacity | Instance Type | Pricing (USD/hr) | Spot Pricing (USD/hr) | CUD Pricing (USD/hr) | Relative Comparison |
|---|---|---|---|---|---|---|---|
| NVIDIA A100 | High-end ML and AI applications | 40 GB GPU Memory | Custom instances | $2.933908 | Varies | Varies | Fastest, Most Expensive |
| NVIDIA T4 | AI inference, light ML workloads | 16 GB GDDR6 | Various | $0.35 | $0.14 | $0.220 | Cheapest, Best Price Performance |
| NVIDIA V100 | High-end ML training, HPC | 16 GB HBM2 | Various | $2.48 | $0.992 | $1.562 | High Performance, Premium Pricing |
| NVIDIA P100 | General-purpose GPU computing | 16 GB HBM2 | Various | $1.46 | $0.584 | $0.919 | Balanced Performance and Cost |
| NVIDIA P4 | Inference, light ML workloads | 8 GB GDDR5 | Various | $0.60 | $0.24 | $0.378 | Moderate Performance, Economical Option |
| NVIDIA K80 | General-purpose GPU computing | 12 GB GDDR5 | Various | $0.45 | $0.18 | Not Available | Good Performance, Budget-Friendly |
| NVIDIA H100 | Cutting-edge AI, HPC | TBD | TBD | TBD | TBD | TBD | Latest Tech, Performance TBD |
| NVIDIA L4 | Entry-level ML, lightweight applications | TBD | TBD | TBD | TBD | TBD | Cost-effective, for light workloads |
| NVIDIA P100 | Enhanced ML, advanced analytics | 16 GB HBM2 | Various | $1.46 | $0.584 | $0.919 | High ML Performance, Reasonable Cost |
| NVIDIA P4 | Inference, small-scale ML | 8 GB GDDR5 | Various | $0.60 | $0.24 | $0.378 | Economical for Inference Tasks |
| NVIDIA T4 | Versatile AI, ML, Data Analytics | 16 GB GDDR6 | Various | $0.35 | $0.14 | $0.220 | Optimal for Diverse AI Workloads |
| NVIDIA V100 | Intensive ML, AI, HPC | 16 GB HBM2 | Various | $2.48 | $0.992 | $1.562 | High-End, for Intensive Computing |
| NVIDIA A100 | Top-tier AI, Deep Learning, HPC | 40 GB GPU Memory | Custom instances | $2.933908 | Varies | Varies | Superior Performance, Premium Price |
GCP NVIDIA GPUs – Use Cases & Comparison
Different GPUs excel in different areas, from Machine Learning and AI applications to high-performance computing. You can make better procurement decisions by understanding the use cases for each GPU.
ML and AI Applications : T4, L4
Basic machine learning and AI applications demand GPUs that can efficiently handle vast datasets and complex algorithms. GPUs in this category are tailored for workloads ranging from basic machine learning tasks to more advanced AI-driven projects, offering the right balance of power and cost.
NVIDIA T4: A balanced performer offering a cost-effective solution for AI and ML workloads.
Ideal for: Entry to mid-level AI-driven applications, analytics, and light AI inference tasks.
Comparison: Offers more cost efficiency than L4, suitable for startups and small to medium businesses.
NVIDIA L4: Entry-level GPU catering to basic machine learning and AI applications.
Ideal for: Small-scale AI projects like basic chatbots and introductory image recognition.
Comparison: More budget-friendly than T4, but with limited processing power.
Deep Learning and Advanced AI : A100, V100
Advanced deep learning tasks require GPUs with exceptional processing capabilities to handle the training of large-scale models and perform intricate computations. These GPUs provide the computational muscle for the most demanding AI applications.
NVIDIA A100: High-end GPU designed for large-scale AI model training and complex computations.
Ideal for: Cutting-edge AI research, large ML models, and data-intensive simulations.
Comparison: Offers peak performance but at a higher cost compared to V100.
NVIDIA V100: A robust choice for intensive deep learning and AI tasks.
Ideal for: Genomics research, high-end 3D rendering, and complex financial computations.
Comparison: Slightly less powerful but more cost-effective than A100.
High-Performance Computing: P100, K80, V100
HPC requires GPUs that can deliver high throughput and handle parallel tasks efficiently.These GPUs are engineered for diverse applications that require significant computational resources.
NVIDIA P100: Balances high ML performance with cost, suitable for a wide range of applications.
Ideal for: Medium to large-scale AI and ML projects, scientific research, and complex data analysis.
Comparison: Strikes a balance between the performance of V100 and the general-purpose utility of K80.
NVIDIA K80:An affordable option for general high-performance computing tasks.
Ideal for: Startups and educational projects needing computational power for basic data processing.
Comparison: More economical than P100, but with lower performance.
Data Analytics: P4, P100, V100
GPUs for data analytics are designed to efficiently process large volumes of data and perform quick analysis, making them ideal for tasks that require rapid ETL processes.
NVIDIA P4: Optimized for light AI tasks and inference workloads.
Ideal for: Basic AI applications in analytics, recommendation systems, and simple image recognition.
Comparison: Less powerful but more cost-effective than P100 and V100.
General Purpose: T4, L4,
General-purpose GPUs are versatile and cater to a broad range of applications. They are ideal for developers who need GPUs that can handle a variety of tasks without specialized requirements.
NVIDIA T4 and L4: These GPUs are versatile choices for various applications, from light AI to basic computing tasks.
Ideal for: Businesses with diverse, moderate-level computational needs.
Comparison: T4 offers better performance than L4, suitable for a wider range of applications.
Whether it’s for machine learning, deep learning, high-performance computing, data analytics, or general-purpose tasks, there’s a GPU optimized for every scenario. As demonstrated by our article on cloud cost case studies, organizations that intelligently leverage FinOps strategies when using cloud GPUs are able to dramatically reduce their costs, and streamline their workflows.
Conclusion
The decision to opt for a particular GPU should be guided by your specific workload requirements, budget constraints, and the potential for cost savings through GCP’s discount options like Sustained Use Discounts, Committed Use Discounts, and Spot VMs Pricing.
A a well-informed choice requires a comprehensive understanding of the available options, their performance benchmarks, and cost implications. We hope this article has elucidated the nuances of each GPU model offered by GCP, from the Nvidia A100 to the T4 and beyond.
How we can help
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